Our mission is to help computational modelers at all levels engage in the establishment and adoption of community standards and good practices for developing and sharing computational models. Model authors can freely publish their model source code in the Computational Model Library alongside narrative documentation, open science metadata, and other emerging open science norms that facilitate software citation, reproducibility, interoperability, and reuse. Model authors can also request peer review of their computational models to receive a DOI.
All users of models published in the library must cite model authors when they use and benefit from their code.
Please check out our model publishing tutorial and contact us if you have any questions or concerns about publishing your model(s) in the Computational Model Library.
We also maintain a curated database of over 7500 publications of agent-based and individual based models with additional detailed metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
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The purpose of this model is to analyze the dynamics of endogenously created oscillations in housing prices using a system dynamics simulation model, built from the perspective of construction companies.
MarPEM is an agent-based model that can be used to study the effects of policy instruments on the transition away from HFO.
Perpetual Motion Machine - A simple economy that operates at both a biophysical and economic level, and is sustainable. The goal: to determine the necessary and sufficient conditions of sustainability, and the attendant necessary trade-offs.
This is the R code of the mathematical model that includes the decision making formulations for artificial agents. Plus, the code for graphical output is also added to the original code.
This is a replication of the altruistic trait selection model described in Pepper & Smuts (2000, 2002).
We construct an agent-based model to investigate and understand the roles of green attachment, engagement in local ecological investment (i.e., greening), and social feedback.
This is a relatively simple foraging-radius model, as described first by Robert Kelly, that allows one to quantify the effect of increased logistical mobility (as represented by increased effective foraging radius, r_e) on the likelihood that 2 randomly placed central place foragers will encounter one another within 5000 time steps.
This model is a replication of that described by Peterson (2002) and illustrates the ‘spread’ feedback loop type described in Millington (2013).
Positive feedback can lead to “trapping” in local optima. Adding a simple negative feedback effect, based on ant behaviour, prevents this trapping
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